Human Symptom-Based Disease Prognosis

Abstract

Modern medicine has led to the discovery of new illnesses with similar or even more severe symptoms than those of earlier eras; nonetheless, these diseases pose a considerably greater threat to human health. So, this approach can detect new illnesses by listening to the user's input, then recognizing the condition and showing it on the user's smart screen. One of the main goals of this effort is to find a way to prevent diseases before they kill people. The illness prediction algorithms used include Naive Bayes and Random Forest. In order to make this forecast, we compare and contrast the two algorithms' accuracy levels and then use the one with the higher level of accuracy to forecast the illness. Python illnesses finish its implementation. Using just machine learning and the Python programming language, this goal is symptom-based. Using the Python T-kinter, the user interface was created. The computer may be taught to forecast the illness utilizing the prior data set that is accessible in hospitals. computer language and the tkinter package.